Background And Objectives: Intrusive memories appear to enter consciousness via involuntary rather than deliberate recollection. Some clinical accounts of PTSD seek to explain this phenomenon by making a clear distinction between the encoding of sensory-based and contextual representations. Contextual representations have been claimed to actively reduce intrusions by anchoring encoded perceptual data for an event in memory. The current analogue trauma study examined this hypothesis by manipulating contextual information independently from encoded sensory-perceptual information.
Method: Participants' viewed images selected from the International Affective Picture System that depicted scenes of violence and bodily injury. Images were viewed either under neutral conditions or paired with contextual information.
Results: Two experiments revealed a significant increase in memory intrusions for images paired with contextual information in comparison to the same images viewed under neutral conditions. In contrast to the observed increase in intrusion frequency there was no effect of contextual representations on voluntary memory for the images. The vividness and emotionality of memory intrusions were also unaffected.
Limitations: The analogue trauma paradigm may fail to replicate the effect of extreme stress on encoding postulated to occur during PTSD.
Conclusions: These findings question the assertion that intrusive memories develop from a lack of integration between sensory-based and contextual representations in memory. Instead it is argued contextual representations play a causal role in increasing the frequency of intrusions by increasing the sensitivity of memory to involuntary retrieval by associated internal and external cues.
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http://dx.doi.org/10.1016/j.jbtep.2011.07.009 | DOI Listing |
Comput Biol Med
January 2025
Department of Artificial Intelligence, Faculty of Artificial Intelligence, Egyptian Russian University, 11829, Badr City, Egypt. Electronic address:
Weakly-supervised learning (WSL) methods have gained significant attention in medical image segmentation, but they often face challenges in accurately delineating boundaries due to overfitting to weak annotations such as bounding boxes. This issue is particularly pronounced in thyroid ultrasound images, where low contrast and noisy backgrounds hinder precise segmentation. In this paper, we propose a novel weakly-supervised segmentation framework that addresses these challenges.
View Article and Find Full Text PDFNeural Netw
January 2025
Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Center of Intelligent Computing, School of Mathematics, East China University of Science and Technology, Shanghai 200237, China. Electronic address:
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. This paper proposes a novel Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network (MS-PSA-SOC) for ERP Detection.
View Article and Find Full Text PDFSci Rep
January 2025
EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.
View Article and Find Full Text PDFNeural Netw
January 2025
State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science, Northeast Electric Power University, Jilin 132012, China.
Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net).
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